Data Science Final Project

library(tidyverse)
library(viridis)
library(ggridges)
library(patchwork)
library(readxl)
library(leaflet)
library(plotly)
library(scales)
library(ggplot2)
library(httr)
library(rvest)

HUGO

Data Importing and Cleaning

years_1 <- c(1900:2012, 2014)
years_2 <- c(2015:2019)
importing_data = function(x){
 
  if(str_detect(x, str_c(years_1, collapse = "|"))) {
  read_csv(x, na = c("NULL", "", "0"), col_types = "cicccciiiicc") 
  } 
  
  else if(str_detect(x, str_c(years_2, collapse = "|"))){
    read_csv(x, na = c("NULL", "", "0"), col_types = "cccicccccccccccccccccciiiiccc")
  }
}
boston_df <- 
  tibble(list.files("data", full.names = TRUE)) %>% 
  setNames("file_name") %>% 
  mutate(data = map(file_name, importing_data)) %>% 
  unnest(data) %>% 
  mutate(year = readr::parse_number(file_name),
        city = coalesce(city, residence),
        display_name = str_replace_all(display_name, "[^a-zA-Z0-9]", " ")) %>% 
  mutate(country_residence = replace(country_residence, country_residence == "AHO", "Netherland Antilles")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ALB", "Albania")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ALG", "Algeria")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "AND", "Andorra")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ARG", "Argentina")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Argenti", "Argentina")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "AUS", "Australia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Austral", "Australia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "AUT", "Austria")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "BAH", "Bahamas")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "BAR", "Barbados")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Barbado", "Barbados")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "BDI", "Burundi")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "BLR", "Belarus")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "BEL", "Belgium")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "BER", "Bermuda")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "BRA", "Brazil")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "BRN", "Brunei")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "BUL", "Bulgaria")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "CAN", "Canada")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "CAY", "Cayman")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "CHI", "Chile")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "CHN", "China")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "COL", "Colombia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Colombi", "Colombia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "CRC", "Costa Rica")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Costa R", "Costa Rica")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "CRO", "Croatia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "CYP", "Cyprus")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "CZE", "Czech Republic")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Czech R", "Czech Republic")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "DEN", "Denmark")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "DOM", "Dominican Republic")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Dominic", "Dominican Republic")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ECU", "Ecuador")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "EGY", "Egypt")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "El Salv", "El Salvador")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ESA", "El Salvador")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ESP", "Spain")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "EST", "Estonia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ETH", "Ethiopia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Ethiopi", "Ethiopia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Faroe I", "Faroe Islands")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "FIN", "Finland")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "FLK", "Falkland Islands")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "FRA", "France")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "GBR", "England")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "GER", "Germany")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "GRE", "Greece")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "GRN", "Greenland")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "GUA", "Guatemala")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Guatema", "Guatemala")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "HKG", "Hong Kong")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Hong Ko", "Hong Kong")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "HON", "Honduras")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Hondura", "Honduras")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "HUN", "Hungary")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "INA", "Indonesia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Indones", "Indonesia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "IND", "India")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "IRL", "Ireland")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ISL", "Iceland")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ISR", "Israel")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ITA", "Italy")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "JAM", "Jamaica")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "JPN", "Japan")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "JOR", "Jordan")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "KEN", "Kenya")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "KOR", "Korea")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Korea,", "Korea")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "KSA", "Saudi Arabia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "KUW", "Kuwait")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "LAT", "Latvia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "LIE", "Liechtenstein")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Liechte", "Liechtenstein")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Lithuan", "Lithuania")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "LTU", "Lithuania")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "LUX", "Luxembourg")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Luxembo", "Luxembourg")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Macao S", "Macao")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Macedon", "Macedonia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Malaysi", "Malaysia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "MAR", "Martinique")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Martini", "Martinique")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "MAS", "Malaysia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "MEX", "Mexico")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "MGL", "Mongolia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "MLT", "Malta")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "NCA", "Nicaragua")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "NED", "Netherlands")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Netherl", "Netherlands")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "New Zea", "New Zealand")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "NGR", "Nigeria")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "NOR", "Norway")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "NZL", "New Zealand")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "OMA", "Oman")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "PAK", "Pakistan")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Palesti", "Palestine")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "PAN", "Panama")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "PAR", "Paraguay")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Paragua", "Paraguay")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "PER", "Peru")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "PHI", "Philippines")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Philipp", "Philippines")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "POL", "Poland")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "POR", "Portugal")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Portuga", "Portugal")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Puerto", "Puerto Rico")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "QAT", "Qatar")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ROU", "Romania")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Saudi A", "Saudi Arabia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "SIN", "Singapore")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Singapo", "Singapore")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "SLO", "Slovenia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Slovaki", "Slovakia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Sloveni", "Slovenia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "SMR", "San Marino")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "South A", "South Africa")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "SRB", "Serbia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Sri Lan", "Sri Lanka")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "SUI", "Switzerland")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "SVK", "Slovakia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "SWE", "Sweden")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Switzer", "Switzerland")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "TCA", "Turks and Caicos")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "THA", "Thailand")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Thailan", "Thailand")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "TPE", "Taipei")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "TRI", "Trinidad")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Trinida", "Trinidad")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "TUR", "Turkey")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "TWN", "Taiwan")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "UAE", "United Arab Emirates")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "UGA", "Uganda")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "UKR", "Ukraine")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "United", "United States")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "URU", "Uruguay")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "USA", "United States")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "VEN", "Venezuela")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Venezue", "Venezuela")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "VGB", "Virgin Islands")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "VIE", "Vietnam")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "ZIM", "Zimbabwe")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "RSA", "South Africa")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "RUS", "Russia")) %>%
  mutate(country_residence = replace(country_residence, country_residence == "Russian", "Russia")) %>%
  filter(!is.na(display_name)) %>% 
  select(-file_name, -residence, -first_name, -last_name)

SUHANI

Geographical Analysis of Champions

Overview

---

We were interested in creating a map with the locations where winners from the Boston marathon over the past 120 year are from. Additionally, we wanted to examine how the locations of winners changed over time. We also analyzed data from winners from the wheel chair division and compared results to the men and women open divsion.

Data Cleaning

---

boston_df2 = boston_df %>% 
  filter(year > 1999) %>% 
  filter(overall == 1) %>% 
  filter(gender == "M") %>% 
  select(year, city, state, overall, everything()) %>% 
  drop_na(city) %>% 
  separate(city, into = c("city", "state", "country"), sep = ",") %>% 
  select(-country, -state) %>% 
  writexl::write_xlsx("interactive_map_men.xlsx")
boston_df3 = boston_df  %>% 
  filter(year > 1999) %>% 
  filter(gender_result == 1) %>% 
  filter(gender == "F") %>% 
  select(year, city, state, overall, everything()) %>% 
  drop_na(city) %>% 
  writexl::write_xlsx("interactive_map_women.xlsx")

map_df = read_excel("data/latitude_longitude_winners.xlsx", sheet = 1) %>% 
  select(year, city, latitude, longitude, age, gender, official_time, display_name) %>% 
  rename(place = city) 
map_df2 = read_excel("data/latitude_longitude_winners.xlsx", sheet = 2) %>% 
  select(year, city, latitude, longitude, age, gender, official_time, display_name) %>% 
  rename(place = city) 

men_open = read_excel("data/geo_winners.xlsx", sheet = 4) %>% 
  janitor::clean_names() %>% 
  filter(!(year == 2013)) %>% 
  rename(place = country) %>% 
  separate(official_time, into = c("data", "official_time"), sep = " (?=[^ ]+$)") %>% 
  select(-data) 
lat_long = read_excel("data/lat_long.xlsx") %>% 
  select(-country) %>% 
  rename(place = name)
men_merge <- merge(men_open,lat_long,by="place") %>% 
  rename(display_name = name) %>% 
  mutate(gender = "M") 
men_merge2 = men_merge %>% 
  mutate(age = NA) %>% 
  filter(year > 1900) %>% 
  filter(year < 2000) 
men_total = rbind(men_merge2, map_df)
women_open = read_excel("data/geo_winners.xlsx", sheet = 3) %>% 
  janitor::clean_names() %>% 
  filter(!(year == 2013)) %>% 
  rename(place = country) %>% 
  separate(official_time, into = c("data", "official_time"), sep = " (?=[^ ]+$)") %>% 
  select(-data) 
women_merge = merge(women_open, lat_long, by="place") %>% 
  rename(display_name = name) %>% 
  mutate(gender = "F") 
  
women_merge2 = women_merge %>% 
  mutate(age = NA) %>% 
  filter(year > 1900) %>% 
  filter(year < 2000) 
woman_total = rbind(women_merge2, map_df2) %>% 
  drop_na(latitude)

women_wheelchair = read_excel("data/geo_winners.xlsx", sheet = "women_wheel_chair") %>% 
    janitor::clean_names() %>% 
    filter(!(year == 2013)) %>% 
    separate(official_time, into = c("data", "official_time"), sep = " (?=[^ ]+$)") %>% 
    select(-data) %>% 
    mutate(gender = "F")
men_wheelchair = read_excel("data/geo_winners.xlsx", sheet = "men_wheel_chair") %>% 
    janitor::clean_names() %>% 
    separate(official_time, into = c("data", "official_time"), sep = " (?=[^ ]+$)") %>% 
    select(-data) %>% 
    mutate(gender = "M")
wheelchair_merge = rbind(women_wheelchair, men_wheelchair) %>% 
  rename(place = country)
wheelchair_total = merge(wheelchair_merge, lat_long, by="place") 
winners_bind = rbind(women_merge, men_merge)

Plots

---

Map of Male Champions Residence

map_winners = leaflet(men_total) %>% 
  addProviderTiles(providers$CartoDB.Positron) %>% 
  addMarkers(lat = ~latitude, lng = ~longitude,
  popup = paste("Name:", men_total$display_name, "<br>", "Year:", men_total$year,"<br>",  "Official Time:", men_total$official_time, "<br>", "Age:", men_total$age, "<br>", "Gender:", men_total$gender), clusterOptions = markerClusterOptions())
map_winners

The continent with the largest nuber of male winners from 1900 and beyond is North American, specifically the US, followed by Africa. There are 44 winners from the United States, 20 from Kenya, 10 from Japan. A large portion of the winners came from Eldoret and Meru in Kenya.

---

Map of Female Champions Residence

map_winners_women = leaflet(woman_total) %>% 
  addProviderTiles(providers$CartoDB.Positron) %>% 
  addMarkers(lat = ~latitude, lng = ~longitude,
  popup = paste("Name:", woman_total$display_name, "<br>", "Year:", woman_total$year,"<br>",  "Official Time:", woman_total$official_time, "<br>", "Age:", woman_total$age, "<br>", "Gender:", woman_total$gender), clusterOptions = markerClusterOptions())
map_winners_women

This is in contrast to female winners from 1900 and beyond where Africa is the continent with the most winners, closely followed by Europe, and North America. Most of the female winners in Africa are from Kenya. The map for female winners suggests that a participants proximinity to Boston likely does not play a role in their probability of winning the marathon.

---

Plot of Official Times and Country of Residence of Champions by Year in the Open Division

plot2 = winners_bind %>%
  mutate(text_label = 
           str_c("Name: ", display_name, "\nGender: ", gender)) %>% 
  plot_ly(
    x = ~year, y = ~official_time, text = ~text_label, color = ~place,
    type = "scatter") %>% 
  layout(
    title = "Official Times of Male and Female Winners each Year by Winner's Country",
    xaxis = list(title = 'Year'),
    yaxis = list(title = 'Marathon Time'), 
    legend = list(title=list(text='Residence')))
plot2

Over time, the winning time for both male and female winners has declined. While prior to 1950, most of the winners were from the United States and Canada, most of the winners are from Kenya. Additionally, we can see that the lowest marathon times have been from people from Kenya. The fastest times have been from people from Kenya, Ethiopia, and the US with one outlier from Australia.

---

Map of Champions in the Wheel Chair Division

map_wheelchair = leaflet(wheelchair_total) %>% 
  addProviderTiles(providers$CartoDB.Positron) %>% 
  addMarkers(lat = ~latitude, lng = ~longitude,
  popup = paste("Name:", wheelchair_total$name, "<br>", "Year:", wheelchair_total$year,"<br>", "Official Time:", wheelchair_total$official_time, "<br>", "Gender:", wheelchair_total$gender), clusterOptions = markerClusterOptions())
map_wheelchair

When assessing wheel chair division map, we can see that 25 winners are from the United States, followed by 19 from Europe, and 10 from Africa. While the male and female open division lack winners from Switzerland, there are 15 winners in the wheel chair division from Switzerland. The fastest time is 1:18 in 2017 by Marcel Hug from Switzerland.

---

Plot of Official Times and Country of Residence of Champions by Year in Wheel Chair Division

plot3 = wheelchair_total %>%
  mutate(text_label = 
           str_c("Name: ", name, "\nGender: ", gender)) %>% 
  plot_ly(
    x = ~year, y = ~official_time, text = ~text_label, color = ~place,
    type = "scatter") %>% 
  layout(
    title = 'Official Times of Winners each Year in Wheel Chair Division',
    xaxis = list(title = 'Year'),
    yaxis = list(title = 'Marathon Time'), 
    legend = list(title=list(text='Residence')))
plot3

While there is a downward trend in official times for the open division, there is no trend in official times over time. Many of the winners pre-2000 are from the United States while many of the winners post-2000 are from South Africa. The lowest time recorded is from an individual in Switzerland.

---

Weather Analysis

Overview

---

We were interested in analyzing the weather patterns such as temperature, wind, and sky conditions during the Boston marathon over time. We were also curious if there was a relationship between weather and the winning time and participant average times.

Data Cleaning

---

weather = read_excel("data/weather_conditions.xlsx") %>% 
  janitor::clean_names() %>% 
  filter(!(year == 2013)) %>% 
  separate(wind, into = c("wind_direction", "wind_speed"), sep = "\\s")  %>% 
  separate(wind_speed, into = c("wind_min", "wind_max"), sep = "-") %>% 
  mutate(wind_min = as.numeric(wind_min)) %>% 
  mutate(wind_max = as.numeric(wind_max)) %>% 
  mutate(wind_speed_average = (wind_min + wind_max)/2)
weather2 = weather %>% 
  select(boston_temp, year, wind_speed_average, sky)

winners_bind2 = winners_bind  %>% 
   filter(year > 1999) 
boston_df2 = boston_df %>% 
     filter(year > 1999) 
  
merge_weather2 <- merge(weather2,winners_bind2,by="year")
merge_weather_participants = merge(weather2,boston_df2,by="year") %>% 
  mutate(boston_temp = as.numeric(boston_temp)) %>% 
  mutate(seconds = as.numeric(seconds)) 

Plots

---

Plot of Weather during Boston Marathons

plot4 = weather %>%
  mutate(text_label = 
           str_c("Wind Speed Average: ", wind_speed_average)) %>%
  plot_ly(x = ~year, y = ~boston_temp, text = ~text_label, color= ~ sky, size = ~wind_speed_average,
          type = "scatter", mode = "markers",  colors = "viridis", 
          sizes = c(50, 700), marker = list(opacity = 0.7)) %>%
  layout(
    title = 'Weather over Time',
    xaxis = list(title = 'Year'),
    yaxis = list(title = 'Boston Temperature'), 
    legend = list(title=list(text='Sky Conditions')))
plot4

*Note datapoint size is related to average wind speed.

Over the past 20 years, the temperature in Boston has generally stayed between 40-70 degrees and is usually a clear day. In 2012, there was a particularly hot marathon day with the tempature in the high 80’s. Over the past few years, there has been relativly low wind speeds on average with exception to 2015.

---

Wind Analysis during Boston Marathons

ggplot(data = weather)+
  geom_segment(aes(x = year, xend = year, y = wind_min, yend = wind_max, colour = wind_direction), size = 5, alpha = 0.6) + 
  labs(
    title = "Wind Speed Range During Boston Marathons Over Time",
    x = "Year",
    y = "Wind Speed Range (mph)",
    color='Wind Direction')

In the early 2000’s, the wind direction was generally toward the North/north east and recently has been generally toward the west, northwest direction. There has been relatively low wind speed with little wind speed varability the past few years during the Boston marathon. Around 2010, there are relatively higher wind speeds. In 2007, there was a particularly windy marathon with speeds ranging from 20-30 mph.

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Comparison between Chamption Times and Weather

plot5 = merge_weather2 %>%
  mutate(text_label = 
           str_c("Name: ", display_name, "\nYear: ", year, "\nResidence: ", place)) %>% 
 plot_ly(
    x = ~boston_temp, y = ~official_time,  text = ~text_label, color = ~sky, size = ~wind_speed_average,
          type = "scatter", mode = "markers",  colors = "viridis", 
          sizes = c(50, 700), marker = list(opacity = 0.7)) %>%
  layout(
    title = 'Champion Times and Weather',
    xaxis = list(title = "Boston's Temperature"),
    yaxis = list(title = 'Official Time'), 
    legend = list(title=list(text='Sky Conditions')))
plot5

*Note datapoint size is related to average wind speed.

The fastest marathon times in the past 20 years have occured when the temperature is around 55 degrees for men and around 60 degrees for woman. At temperatures 55 and below as well as 85 and above, there tends to be higher wind speeds. Although there are only a few datapoints above 70 degrees, one may hypothesize from the graph that as temperature increases, the fastest marathon time also increases. The fastest time was run during medium wind speed, 55 degrees and a clear sky.

---

Comparison between Participants Times and Weather

plot6 = merge_weather_participants %>% 
   plot_ly(x = ~boston_temp, y = ~seconds, color = ~sky, type = "box") %>% 
  layout(
    title = 'Participants Times by Temperature',
    xaxis = list(title = "Boston's Temperature"),
    yaxis = list(title = 'Official Time (seconds)'), 
    legend = list(title=list(text='Sky Conditions')))
plot6

As temperature increases, the median marathon time among participants also increases also slightly increases. Furthermore, the lowested median marathon times occur around 55 degrees. According to a study called “Import of Environmental Parameters on Marathon Running Performance”, the ideas tempature for professional runners is 37 degrees but for nonprofessional athletes is around 50 degrees. We also see that there are many outliers in the higher timepoints because there are likely a handful of very slow runners.

TAMARA

cleaning data for winner over time plots

We are interested in looking at the trends of the winning Boston Marathon times from the 1900’s to 2019. To do this we first needed to convert data and time variables from character to POSIXCT.

plotly_win_df = 
  boston_df %>% 
    mutate(
      official_time = as.POSIXct(official_time, format = "%H:%M:%OS"), 
      year = as.factor(year),
      pace = as.POSIXct(pace, format = "%H:%M:%OS"),
      place_overall = as.numeric(place_overall)
    )

From here with the cleaned up variables, we filtered the data set to just show the overall winners.

plotly_win_df %>% 
  filter(overall == 1) %>% 
  plot_ly(x = ~year, y = ~format(official_time, "%H:%M:%OS"), 
          mode = 'lines', type = 'scatter',
          name = 'Boston Winners',
          hoverinfo = "text",
          text = 
            ~paste0("Name: ", display_name,
                    "\n", "Year: ", year,
            "\n", "Time: ", format(official_time, "%H:%M:%OS"),
            "\n", "Pace: ", format(pace, "%H:%M:%OS"))) %>% 
  layout(
    title = "Boston Marathon Winners by Year",
         xaxis = list(title = "Year"),
         yaxis = list(title = "Official Time"))

This plot shows that over time, the Boston marathon winners are getting faster. There also seems to be some data that was recorded incorectly. Jim Knaub and Franz Nietlispach are reported as having an official time of 1:22:17 and 1:25:59 but the fastest marathon ever run is 1:59 (though not recorded officially, https://www.nytimes.com/2019/10/12/sports/eliud-kipchoge-marathon-record.html)

The fastest time ever run (excluding the sub two hour times) for the Boston Marathon was run by Martin E Duffy in 1975, with a time of 2:04:54.

How do the Boston Marathon winners compare to the record marathon time for the same year?

To compare the Boston Marathon with the record marathon times, we found a table from www.topendsports.com where they recorded the fastest marathon time for that year starting from 1908 to 2018 (though not every year is included).

The plot shows the dramatic decrease in fastest recorded marathon times over the years. In 1908 the fastest recorded time was run by Johnny Hayes in the London Marathon in 2:55:18, compared to the current record holder, Eliud Kipchoge, running the 2:01:39 in the Berlin Marathon in 2018.

records_html = 
  read_html("https://www.topendsports.com/sport/athletics/record-marathon.htm")


record_marathon =
  records_html %>% 
  html_nodes("table") %>% 
  html_table(fill = T) %>% 
  lapply(., function(x) setNames(x, c("time", "date", "athlete", "country", "marathon"))) 

marathon_records = 
record_marathon %>% 
  as.data.frame() %>% 
  mutate(
    time = as.POSIXct(time, format = "%H:%M:%OS"),
  ) %>% 
  separate(date, into = c("month", "day", "year")) %>% 
    mutate(year = as.numeric(year)) %>% 
    select(-month, -day)


marathon_records %>% 
  plot_ly(
    x = ~year, y = ~format(time, "%H:%M:%OS"),
    mode = 'lines', type = 'scatter',
    name = "Marathon Record Times",
    hoverinfo = "text", text = ~paste0(
        "Name: ", athlete, 
        "\n", "Year: ", year,
        "\n", "Time: ", format(time, "%H:%M:%OS"),
        "\n", "Marathon: ", marathon)) %>% 
  layout(
    title = "Record Marathon Times by Year",
         xaxis = list(title = "Year"),
         yaxis = list(title = "Official Time")) 

CLAIRE

Prepare Age Data For Plots

boston_df_age =
  boston_df %>% 
  select(age) %>%
  drop_na(age)

Age Distribution of Participants in Counts

age_plotly = 
  boston_df_age %>% 
  count(age) %>%
  plot_ly(
      x = ~age, y = ~n, color = ~age,
      type = "bar", colors = "viridis") %>%
  layout(
    xaxis = list(title = "Age"),
    yaxis = list(title = "Number of Participants"))

age_plotly 

To create a bar chart of the age distribution for the Boston Marathon from 1900-2019, we began by selecting the age variable and dropping missing age values. Then, we used plotly to create an interactive plot that shows the age on the x axis and the number of participants at each age year on the y axis. Additionally, there is an option to hover over each bar within the chart to see the exact age and number of participants at that age.

We notice that the majority of participants in the Boston marathon from 1900-2019 are 35-50 years old, with the most common age being 40. Approximately 21,000 of our 404,727 participants are 40 years old. We also notice that at 5 year age increments, from 40 to 60 years old, ie. 45, 50, 55, there appears to be a higher number of participants compared with the surrounding age years.

Age Distribution of Participants in %’s

age_percentage_ggplot = 
  ggplot(boston_df_age, 
       aes(x = age, 
           y = ..count.. / sum(..count..))) +
  geom_histogram(fill = "cornflowerblue", 
                 color = "white", 
                 binwidth = 10) + 
  labs(title = "Participants by age", 
       y = "Percent",
       x = "Age") + 
  scale_x_continuous(breaks = seq(5, 85, by = 10)) + 
  scale_y_continuous(labels = percent)

This histogram shows the age distribution of participants in %’s of total participants in 10 year segments. We see that the age range of 35-45, there is the highest percentage of participants, approximately 33%.

Prepare Gender Data For Plots

? move to top ?

mutate(gender = na_if(gender, “U”), gender = recode(gender, m = “M”), gender = factor(gender, levels = c(“M”, “F”), labels = c(“male”, “female”))) ###

Begin gender plots

boston_df_gender =
    boston_df %>%
    select(gender) %>%
    mutate(gender = na_if(gender, "U"), 
            gender = recode(gender, m = "M"),
           gender = factor(gender, levels = c("M", "F"), labels = c("male", "female"))) %>% 
  drop_na(gender) %>% 
  count(gender)

Participants By Gender (%’s)

gender_bar_graph = boston_df_gender %>%
  mutate(pct = (n / sum(n)),
         pctlabel = paste0(round(pct*100), "%")
  )

ggplot(gender_bar_graph, 
       aes(x = reorder(gender, -pct),
           y = pct, fill = gender)) + 
  geom_bar(stat = "identity") +
  geom_text(aes(label = pctlabel), 
            vjust = -0.25) +
  scale_y_continuous(labels = percent) +
  labs(x = "Gender", 
       y = "Percent", 
       title  = "Percent of participants by gender")

To create a bar chart for gender distribution, we selected the gender variable, dropped missing values, created a new variable to find the percent of the total that lie within each gender category, by computing n/sum(n), then created a variable that converts the pct variable into a percentage to utilize in the plot.

The graph shows that from 1900-2019, approximately 66% of Marathon participants were male and 34% of Marathon runners were female. There is a large discrepancy between genders.

Prepare Gender Count Change Over Time Data

gender_timeplot_df =
    boston_df %>%
    select(year, gender) %>%
    mutate(gender = na_if(gender, "U"), 
            gender = recode(gender, m = "M"),
           gender = factor(gender, levels = c("M", "F"), labels = c("male", "female"))) %>% 
  drop_na(gender) %>%
  drop_na(year) %>%
  group_by(year, gender) %>%
  count()

Gender Distribution over Time

gender_timeplot =
ggplot(gender_timeplot_df, aes(x = year, y = n)) + 
  geom_line(aes(color = gender), size = 1) + xlim(1900, 2019) + labs(x = "Year", 
       y = "Participants", 
       title  = "Number of participants over time by gender")

ggplotly(gender_timeplot)

We may wonder if the number of participants in each category has changed over time, and if so by how much? To determine how the distribution of male and female participants has changed over time. We grouped by year and gender, then used counts to determine the number of participants at each year for each gender. Using plotly, you can hover over to see each year, number of participants, and the gender.

The plot reveals that female participation did not begin until ~1972, while male participation began in 1900 - according to our dataset. While there has been an increase in female runners, there is still approximately a ~2,500 participant discrepancy between gender participation. Also of interest is that the graph shows that in 1996, there was a steep increase in participants among both males and females, and this is likely because this was the 100th anniversary of the race.

Prepare country data over time for plot

country_overtime_df = 
  boston_df %>% 
  select(country_residence, year) %>%
  drop_na(country_residence) %>%
  group_by(year, country_residence) %>%
  count() %>%
  filter(n > 200) %>%
  arrange()

country_overtime_plot =
ggplot(country_overtime_df, aes(x = year, y = n)) + 
  geom_line(aes(color = country_residence), size = 1) + xlim(2016, 2019) + labs(x = "Year", 
       y = "Number of Participants", 
       title  = "Number of Participants over time by country")

Popular Countries for Participation

country_counts_df = 
  boston_df %>% 
    select(country_residence) %>% 
    drop_na(country_residence) %>% 
    group_by(country_residence) %>%
    count() %>%
    filter(n > 500) 

Plot

popular_country_US = 
 country_counts_df %>% 
 plot_ly(
      x = ~country_residence, y = ~n, color = ~country_residence,
      type = "bar", colors = "viridis") %>% 
  layout(
    xaxis = list(title = "Country"),
    yaxis = list(title = "Number of Participants"))
    
popular_country_US

Remove the United States

popular_country_noUS = 
  country_counts_df %>% 
  filter(country_residence != "United States") %>%
  plot_ly(
      x = ~country_residence, y = ~n, color = ~country_residence,
      type = "bar", colors = "viridis") %>% 
  layout(
    xaxis = list(title = "Country"),
    yaxis = list(title = "Number of Participants"))

popular_country_noUS

Remove the United States and Canada

popular_country_others = 
 country_counts_df %>% 
  filter(country_residence != "United States") %>%
  filter(country_residence != "Canada") %>%
 plot_ly(
      x = ~country_residence, y = ~n, color = ~country_residence,
      type = "bar", colors = "viridis") %>% 
  layout(
    xaxis = list(title = "Age"),
    yaxis = list(title = "Number of Participants"))

popular_country_others

SYDNEY